{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\pysal\\explore\\segregation\\network\\network.py:16: UserWarning: You need pandana and urbanaccess to work with segregation's network module\n",
      "You can install them with  `pip install urbanaccess pandana` or `conda install -c udst pandana urbanaccess`\n",
      "  \"You need pandana and urbanaccess to work with segregation's network module\\n\"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pysal as ps\n",
    "from sklearn import cluster\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import scale\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "sns.set(style=\"whitegrid\")\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. PCA analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = pd.read_excel(r\"/df.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = df_1.set_index('name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metropolis and province</th>\n",
       "      <th>city and district</th>\n",
       "      <th>region</th>\n",
       "      <th>apt</th>\n",
       "      <th>apt_ratio</th>\n",
       "      <th>capital_region</th>\n",
       "      <th>honam_region</th>\n",
       "      <th>youngnam_region</th>\n",
       "      <th>gangwon_region</th>\n",
       "      <th>chungcheong_region</th>\n",
       "      <th>...</th>\n",
       "      <th>gender_ratio</th>\n",
       "      <th>urbanization</th>\n",
       "      <th>turn_out</th>\n",
       "      <th>electors</th>\n",
       "      <th>votes</th>\n",
       "      <th>conservative</th>\n",
       "      <th>conservative/electors</th>\n",
       "      <th>democratic</th>\n",
       "      <th>democratic/electors</th>\n",
       "      <th>conserva_win</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chuncheon-si</th>\n",
       "      <td>강원도</td>\n",
       "      <td>춘천시</td>\n",
       "      <td>Gangwon region</td>\n",
       "      <td>33.62</td>\n",
       "      <td>0.699751</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>97.5</td>\n",
       "      <td>81.50</td>\n",
       "      <td>0.766819</td>\n",
       "      <td>243403</td>\n",
       "      <td>186646</td>\n",
       "      <td>94926</td>\n",
       "      <td>0.389995</td>\n",
       "      <td>82376</td>\n",
       "      <td>0.338435</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yeongam-gun</th>\n",
       "      <td>전라남도</td>\n",
       "      <td>영암군</td>\n",
       "      <td>Honam region</td>\n",
       "      <td>99.72</td>\n",
       "      <td>0.396137</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>108.0</td>\n",
       "      <td>55.75</td>\n",
       "      <td>0.802739</td>\n",
       "      <td>46507</td>\n",
       "      <td>37333</td>\n",
       "      <td>4092</td>\n",
       "      <td>0.087987</td>\n",
       "      <td>31909</td>\n",
       "      <td>0.686112</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hadong-gun</th>\n",
       "      <td>경상남도</td>\n",
       "      <td>하동군</td>\n",
       "      <td>Youngnam region</td>\n",
       "      <td>132.12</td>\n",
       "      <td>0.699519</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99.8</td>\n",
       "      <td>22.38</td>\n",
       "      <td>0.809511</td>\n",
       "      <td>39640</td>\n",
       "      <td>32089</td>\n",
       "      <td>18974</td>\n",
       "      <td>0.478658</td>\n",
       "      <td>11218</td>\n",
       "      <td>0.282997</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boeun-gun</th>\n",
       "      <td>충청북도</td>\n",
       "      <td>보은군</td>\n",
       "      <td>Chungcheong region</td>\n",
       "      <td>132.62</td>\n",
       "      <td>0.201373</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>101.7</td>\n",
       "      <td>46.29</td>\n",
       "      <td>0.784129</td>\n",
       "      <td>28971</td>\n",
       "      <td>22717</td>\n",
       "      <td>12754</td>\n",
       "      <td>0.440233</td>\n",
       "      <td>8757</td>\n",
       "      <td>0.302268</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cheongyang-gun</th>\n",
       "      <td>충청남도</td>\n",
       "      <td>청양군</td>\n",
       "      <td>Chungcheong region</td>\n",
       "      <td>134.69</td>\n",
       "      <td>0.453522</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>103.3</td>\n",
       "      <td>32.57</td>\n",
       "      <td>0.777834</td>\n",
       "      <td>27754</td>\n",
       "      <td>21588</td>\n",
       "      <td>12932</td>\n",
       "      <td>0.465951</td>\n",
       "      <td>7688</td>\n",
       "      <td>0.277005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               metropolis and province city and district              region  \\\n",
       "name                                                                           \n",
       "Chuncheon-si                       강원도               춘천시      Gangwon region   \n",
       "Yeongam-gun                       전라남도               영암군        Honam region   \n",
       "Hadong-gun                        경상남도               하동군     Youngnam region   \n",
       "Boeun-gun                         충청북도               보은군  Chungcheong region   \n",
       "Cheongyang-gun                    충청남도               청양군  Chungcheong region   \n",
       "\n",
       "                   apt  apt_ratio  capital_region  honam_region  \\\n",
       "name                                                              \n",
       "Chuncheon-si     33.62   0.699751               0             0   \n",
       "Yeongam-gun      99.72   0.396137               0             1   \n",
       "Hadong-gun      132.12   0.699519               0             0   \n",
       "Boeun-gun       132.62   0.201373               0             0   \n",
       "Cheongyang-gun  134.69   0.453522               0             0   \n",
       "\n",
       "                youngnam_region  gangwon_region  chungcheong_region  ...  \\\n",
       "name                                                                 ...   \n",
       "Chuncheon-si                  0               1                   0  ...   \n",
       "Yeongam-gun                   0               0                   0  ...   \n",
       "Hadong-gun                    1               0                   0  ...   \n",
       "Boeun-gun                     0               0                   1  ...   \n",
       "Cheongyang-gun                0               0                   1  ...   \n",
       "\n",
       "                gender_ratio  urbanization  turn_out  electors   votes  \\\n",
       "name                                                                     \n",
       "Chuncheon-si            97.5         81.50  0.766819    243403  186646   \n",
       "Yeongam-gun            108.0         55.75  0.802739     46507   37333   \n",
       "Hadong-gun              99.8         22.38  0.809511     39640   32089   \n",
       "Boeun-gun              101.7         46.29  0.784129     28971   22717   \n",
       "Cheongyang-gun         103.3         32.57  0.777834     27754   21588   \n",
       "\n",
       "                conservative  conservative/electors  democratic  \\\n",
       "name                                                              \n",
       "Chuncheon-si           94926               0.389995       82376   \n",
       "Yeongam-gun             4092               0.087987       31909   \n",
       "Hadong-gun             18974               0.478658       11218   \n",
       "Boeun-gun              12754               0.440233        8757   \n",
       "Cheongyang-gun         12932               0.465951        7688   \n",
       "\n",
       "                democratic/electors  conserva_win  \n",
       "name                                               \n",
       "Chuncheon-si               0.338435             1  \n",
       "Yeongam-gun                0.686112             0  \n",
       "Hadong-gun                 0.282997             1  \n",
       "Boeun-gun                  0.302268             1  \n",
       "Cheongyang-gun             0.277005             1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['metropolis and province', 'city and district', 'region', 'apt',\n",
       "       'apt_ratio', 'capital_region', 'honam_region', 'youngnam_region',\n",
       "       'gangwon_region', 'chungcheong_region', 'education', 'high_education',\n",
       "       'home_owner', 'house', 'mean_age', 'gender_ratio', 'urbanization',\n",
       "       'turn_out', 'electors', 'votes', 'conservative',\n",
       "       'conservative/electors', 'democratic', 'democratic/electors',\n",
       "       'conserva_win'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.columns.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = df_1[['apt','apt_ratio', 'capital_region', 'honam_region', 'youngnam_region',\n",
    "       'gangwon_region', 'chungcheong_region', 'high_education',\n",
    "        'mean_age', 'gender_ratio', 'urbanization', 'turn_out', 'home_owner', ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99732943, 0.00251374])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_1 = PCA(n_components = 2)\n",
    "printcipalComponents = pca_1.fit_transform(df_2)\n",
    "\n",
    "pca_1 = pca_1.explained_variance_ratio_\n",
    "pca_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:1487: MatplotlibDeprecationWarning: normalize=None does not normalize if the sum is less than 1 but this behavior is deprecated since 3.3 until two minor releases later. After the deprecation period the default value will be normalize=True. To prevent normalization pass normalize=False \n",
      "  results = ax.pie(y, labels=blabels, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_v = pd.DataFrame(pca_1, index=['PC1','PC2'], columns=[' '])\n",
    "\n",
    "my_colors = ['gold','whitesmoke']\n",
    "my_explode = (0.1, 0)\n",
    "wedgeprops={'width': 0.7, 'edgecolor': 'w', 'linewidth': 5}\n",
    "\n",
    "\n",
    "df_v.plot.pie(y = ' ',autopct = '%1.1f%%', startangle=15, colors=my_colors, explode=my_explode,\n",
    "              textprops={'fontsize': 16}, wedgeprops=wedgeprops)\n",
    "\n",
    "plt.legend(fontsize='x-large',loc='center left', bbox_to_anchor=(0.8, 0.9))\n",
    "\n",
    "\n",
    "#plt.title('PCA 1 components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_3 = df_1[['apt','apt_ratio', 'capital_region', 'honam_region', 'youngnam_region',\n",
    "       'gangwon_region', 'chungcheong_region', 'high_education',\n",
    "        'mean_age', 'gender_ratio', 'urbanization', 'turn_out', 'home_owner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.97329428e-01, 2.51374368e-03, 1.25853326e-04])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_2 = PCA(n_components = 3)\n",
    "printcipalComponents = pca_2.fit_transform(df_3)\n",
    "\n",
    "pca_2 = pca_2.explained_variance_ratio_\n",
    "pca_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:1487: MatplotlibDeprecationWarning: normalize=None does not normalize if the sum is less than 1 but this behavior is deprecated since 3.3 until two minor releases later. After the deprecation period the default value will be normalize=True. To prevent normalization pass normalize=False \n",
      "  results = ax.pie(y, labels=blabels, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_d = pd.DataFrame(pca_2, index=['PC1','PC2','PC3'], columns=[' '])\n",
    "\n",
    "my_colors = ['gold','whitesmoke', 'lightgray']\n",
    "my_explode = (0.1, 0, 0)\n",
    "wedgeprops={'width': 0.7, 'edgecolor': 'w', 'linewidth': 5}\n",
    "\n",
    "\n",
    "df_d.plot.pie(y = ' ',autopct = '%1.1f%%', startangle=15, colors=my_colors, explode=my_explode,\n",
    "              textprops={'fontsize': 12}, wedgeprops=wedgeprops)\n",
    "#plt.title('PCA 1 components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. K-means++ analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import data for PCA\n",
    "\n",
    "df_1 = pd.read_excel(r\"/df.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Indexing based on name\n",
    "\n",
    "df_1 = df_1.set_index('name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Makeing df_2 based on variables for PCA\n",
    "\n",
    "df_2 = df_1[['apt','apt_ratio', 'capital_region', 'honam_region', 'youngnam_region',\n",
    "       'gangwon_region', 'chungcheong_region', 'high_education',\n",
    "        'mean_age', 'gender_ratio', 'urbanization', 'turn_out', 'home_owner', ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## PCA analysis and make new variable PC 1\n",
    "\n",
    "pca = PCA(n_components=1)\n",
    "pca.fit(df_2)\n",
    "df_pca_1 = pca.transform(df_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Making PC 1 as \"regional_cha\"\n",
    "\n",
    "pca_columns = ['regionl_cha']\n",
    "df_region = pd.DataFrame(df_pca_1, columns = pca_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = pd.read_excel(r\"/df.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Merging data PC 1 and df_1\n",
    "\n",
    "df_region = pd.merge(df_1['name'], df_region , left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>regionl_cha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chuncheon-si</td>\n",
       "      <td>-468.717054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yeongam-gun</td>\n",
       "      <td>-403.496394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hadong-gun</td>\n",
       "      <td>-372.089540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name  regionl_cha\n",
       "0  Chuncheon-si  -468.717054\n",
       "1   Yeongam-gun  -403.496394\n",
       "2    Hadong-gun  -372.089540"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_region.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import data for voting variable\n",
    "\n",
    "df_1 = pd.read_excel(r\"/df.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metropolis and province</th>\n",
       "      <th>city and district</th>\n",
       "      <th>region</th>\n",
       "      <th>name</th>\n",
       "      <th>apt</th>\n",
       "      <th>apt_ratio</th>\n",
       "      <th>capital_region</th>\n",
       "      <th>honam_region</th>\n",
       "      <th>youngnam_region</th>\n",
       "      <th>gangwon_region</th>\n",
       "      <th>...</th>\n",
       "      <th>gender_ratio</th>\n",
       "      <th>urbanization</th>\n",
       "      <th>turn_out</th>\n",
       "      <th>electors</th>\n",
       "      <th>votes</th>\n",
       "      <th>conservative</th>\n",
       "      <th>conservative/electors</th>\n",
       "      <th>democratic</th>\n",
       "      <th>democratic/electors</th>\n",
       "      <th>conserva_win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>강원도</td>\n",
       "      <td>춘천시</td>\n",
       "      <td>Gangwon region</td>\n",
       "      <td>Chuncheon-si</td>\n",
       "      <td>33.62</td>\n",
       "      <td>0.699751</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>97.5</td>\n",
       "      <td>81.50</td>\n",
       "      <td>0.766819</td>\n",
       "      <td>243403</td>\n",
       "      <td>186646</td>\n",
       "      <td>94926</td>\n",
       "      <td>0.389995</td>\n",
       "      <td>82376</td>\n",
       "      <td>0.338435</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>전라남도</td>\n",
       "      <td>영암군</td>\n",
       "      <td>Honam region</td>\n",
       "      <td>Yeongam-gun</td>\n",
       "      <td>99.72</td>\n",
       "      <td>0.396137</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>108.0</td>\n",
       "      <td>55.75</td>\n",
       "      <td>0.802739</td>\n",
       "      <td>46507</td>\n",
       "      <td>37333</td>\n",
       "      <td>4092</td>\n",
       "      <td>0.087987</td>\n",
       "      <td>31909</td>\n",
       "      <td>0.686112</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>경상남도</td>\n",
       "      <td>하동군</td>\n",
       "      <td>Youngnam region</td>\n",
       "      <td>Hadong-gun</td>\n",
       "      <td>132.12</td>\n",
       "      <td>0.699519</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>99.8</td>\n",
       "      <td>22.38</td>\n",
       "      <td>0.809511</td>\n",
       "      <td>39640</td>\n",
       "      <td>32089</td>\n",
       "      <td>18974</td>\n",
       "      <td>0.478658</td>\n",
       "      <td>11218</td>\n",
       "      <td>0.282997</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  metropolis and province city and district           region          name  \\\n",
       "0                     강원도               춘천시   Gangwon region  Chuncheon-si   \n",
       "1                    전라남도               영암군     Honam region   Yeongam-gun   \n",
       "2                    경상남도               하동군  Youngnam region    Hadong-gun   \n",
       "\n",
       "      apt  apt_ratio  capital_region  honam_region  youngnam_region  \\\n",
       "0   33.62   0.699751               0             0                0   \n",
       "1   99.72   0.396137               0             1                0   \n",
       "2  132.12   0.699519               0             0                1   \n",
       "\n",
       "   gangwon_region  ...  gender_ratio  urbanization  turn_out  electors  \\\n",
       "0               1  ...          97.5         81.50  0.766819    243403   \n",
       "1               0  ...         108.0         55.75  0.802739     46507   \n",
       "2               0  ...          99.8         22.38  0.809511     39640   \n",
       "\n",
       "    votes  conservative  conservative/electors  democratic  \\\n",
       "0  186646         94926               0.389995       82376   \n",
       "1   37333          4092               0.087987       31909   \n",
       "2   32089         18974               0.478658       11218   \n",
       "\n",
       "   democratic/electors  conserva_win  \n",
       "0             0.338435             1  \n",
       "1             0.686112             0  \n",
       "2             0.282997             1  \n",
       "\n",
       "[3 rows x 26 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_apt = df_1[['name','conservative/electors']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 K-means++ analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_0 = pd.merge(df_apt, df_region,on=['name',], how='outer')\n",
    "df_0.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = df_0.set_index('name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Write a function that takes a number of clustering counts as a List and visualizes each silhouette coefficient.\n",
    "def visualize_silhouette(cluster_lists, X_features): \n",
    "    \n",
    "    from sklearn.datasets import make_blobs\n",
    "    from sklearn.cluster import KMeans\n",
    "    from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "    import matplotlib.cm as cm\n",
    "    import math\n",
    "    \n",
    "    sns.set(style='whitegrid')\n",
    "\n",
    "    \n",
    "    # Takes a list of clustering counts as input, applies clustering to each count, and gets the number of silhouettes.\n",
    "    n_cols = len(cluster_lists)\n",
    "    \n",
    "    # Generate axs with as many sub-figures as the number of clusters listed with plt.subplots() \n",
    "    fig, axs = plt.subplots(figsize=(4*n_cols, 4), nrows=1, ncols=n_cols)\n",
    "    \n",
    "    # Visualize the number of silhouettes while iterating over the listed clustering counts.\n",
    "    for ind, n_cluster in enumerate(cluster_lists):\n",
    "        \n",
    "        # Perform KMeans clustering, compute silhouette score and silhouette value of individual data. \n",
    "        clusterer = KMeans(n_clusters = n_cluster, max_iter=500, random_state=0)\n",
    "        cluster_labels = clusterer.fit_predict(X_features)\n",
    "        \n",
    "        sil_avg = silhouette_score(X_features, cluster_labels)\n",
    "        sil_values = silhouette_samples(X_features, cluster_labels)\n",
    "\n",
    "        n_samples, n_features = X_features.shape\n",
    "        n_clusters = len(np.unique(cluster_labels))\n",
    "\n",
    "        y_lower = 12\n",
    "        axs[ind].set_title('Number of Cluster : '+ str(n_cluster)+'\\n' \\\n",
    "                          'Silhouette Score :' + str(round(sil_avg,4)), fontsize=16 )\n",
    "        axs[ind].set_xlabel(\"The silhouette coefficient values\", fontsize=13)\n",
    "        axs[ind].set_ylabel(\"Cluster label\", fontsize=12)\n",
    "\n",
    "        axs[ind].set_ylim([0, n_samples + (n_clusters + 1) * 10])\n",
    "        axs[ind].set_yticks([])  # Clear the yaxis labels / ticks\n",
    "        \n",
    "        # A bar graph representation in the form of fill_betweenx( ) by number of clusters. \n",
    "        for i in range(n_cluster):\n",
    "            ith_cluster_sil_values = sil_values[cluster_labels==i]\n",
    "            ith_cluster_sil_values.sort()\n",
    "            \n",
    "            size_cluster_i = ith_cluster_sil_values.shape[0]\n",
    "            y_upper = y_lower + size_cluster_i\n",
    "            \n",
    "            color = cm.nipy_spectral(float(i) / n_cluster)\n",
    "            axs[ind].fill_betweenx(np.arange(y_lower, y_upper), 0, ith_cluster_sil_values, \\\n",
    "                                facecolor=color, edgecolor=color, alpha=0.7)\n",
    "            axs[ind].text(-0.0009, y_lower + 0.01 * size_cluster_i, str(i))\n",
    "            y_lower = y_upper + 10\n",
    "            \n",
    "        axs[ind].axvline(x=sil_avg, color=\"red\", linestyle=\"--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Receive multiple clustering numbers as a List and visualize each clustering result. \n",
    "def visualize_kmeans_plot_multi(cluster_lists, X_features):\n",
    "    \n",
    "    from sklearn.cluster import KMeans\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    \n",
    "    # generate axs with as many sub-figures as the listed clustering with plt.subplots() \n",
    "    n_cols = len(cluster_lists)\n",
    "    fig, axs = plt.subplots(figsize=(4*n_cols, 4), nrows=1, ncols=n_cols)\n",
    "    \n",
    "    \n",
    "    # Perform and visualize KMeans clustering while iterating over the number of clusters listed.\n",
    "    for ind, n_cluster in enumerate(cluster_lists):\n",
    "        \n",
    "        # Save clustering results to a dataframe with KMeans clustering. \n",
    "        clusterer = KMeans(n_clusters = n_cluster, max_iter=500, random_state=0)\n",
    "        cluster_labels = clusterer.fit_predict(df_1)\n",
    "        df_1['kmeans_label']=cluster_labels\n",
    "        \n",
    "        unique_labels = np.unique(clusterer.labels_)\n",
    "        markers=['o', 's', '^', 'x', '*', '>', 'p']\n",
    "\n",
    "        \n",
    "        # Visualize each clustering result with a scatter plot\n",
    "        for label in unique_labels:\n",
    "            label_df = df_1[df_1['kmeans_label']==label]\n",
    "            if label == -1:\n",
    "                cluster_legend = 'Noise'\n",
    "            else :\n",
    "                cluster_legend = 'Cluster '+str(label)           \n",
    "            axs[ind].scatter(x=label_df['regionl_cha'], y=label_df['conservative/electors'], s=90,\\\n",
    "                        edgecolor='k', marker=markers[label], label=cluster_legend)\n",
    "        \n",
    "\n",
    "        axs[ind].set_title('Number of Cluster : '+ str(n_cluster), size = 18)    \n",
    "        axs[ind].legend(loc='lower right')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_silhouette([2,3,4,5,6,7],df_1)\n",
    "visualize_kmeans_plot_multi([2,3,4,5,6,7],df_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
